Overview

Dataset statistics

Number of variables15
Number of observations3747
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory468.4 KiB
Average record size in memory128.0 B

Variable types

Numeric12
Categorical3

Alerts

citric acid is highly overall correlated with citric acid presentHigh correlation
residual sugar is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with densityHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxideHigh correlation
density is highly overall correlated with residual sugar and 2 other fieldsHigh correlation
alcohol is highly overall correlated with density and 1 other fieldsHigh correlation
quality is highly overall correlated with quality categoryHigh correlation
quality category is highly overall correlated with qualityHigh correlation
citric acid present is highly overall correlated with citric acidHigh correlation
body type is highly overall correlated with alcoholHigh correlation
quality category is highly imbalanced (50.6%)Imbalance
citric acid present is highly imbalanced (88.0%)Imbalance
body type is highly imbalanced (63.2%)Imbalance
citric acid has 61 (1.6%) zerosZeros

Reproduction

Analysis started2023-05-10 21:25:33.804299
Analysis finished2023-05-10 21:25:48.631289
Duration14.83 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct58
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9326661
Minimum3.8
Maximum9.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2023-05-10T21:25:48.693913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.6
Q16.3
median6.8
Q37.5
95-th percentile8.7
Maximum9.8
Range6
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.92824325
Coefficient of variation (CV)0.13389412
Kurtosis0.309638
Mean6.9326661
Median Absolute Deviation (MAD)0.6
Skewness0.47051731
Sum25976.7
Variance0.86163552
MonotonicityNot monotonic
2023-05-10T21:25:48.805731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 198
 
5.3%
6.8 198
 
5.3%
6.4 191
 
5.1%
6.9 179
 
4.8%
7 171
 
4.6%
6.7 157
 
4.2%
7.1 150
 
4.0%
7.2 148
 
3.9%
6.5 144
 
3.8%
7.4 139
 
3.7%
Other values (48) 2072
55.3%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.4 3
 
0.1%
4.6 2
 
0.1%
4.7 3
 
0.1%
4.8 9
 
0.2%
4.9 5
 
0.1%
5 20
0.5%
5.1 20
0.5%
5.2 26
0.7%
ValueCountFrequency (%)
9.8 14
0.4%
9.7 6
 
0.2%
9.6 10
 
0.3%
9.5 9
 
0.2%
9.4 15
0.4%
9.3 11
0.3%
9.2 22
0.6%
9.1 12
0.3%
9 25
0.7%
8.9 24
0.6%

volatile acidity
Real number (ℝ)

Distinct110
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30995463
Minimum0.08
Maximum0.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2023-05-10T21:25:48.924270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.1565
Q10.22
median0.28
Q30.37
95-th percentile0.59
Maximum0.68
Range0.6
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.12722409
Coefficient of variation (CV)0.41046037
Kurtosis0.45074084
Mean0.30995463
Median Absolute Deviation (MAD)0.07
Skewness1.0084559
Sum1161.4
Variance0.01618597
MonotonicityNot monotonic
2023-05-10T21:25:49.033063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 176
 
4.7%
0.24 175
 
4.7%
0.26 166
 
4.4%
0.25 148
 
3.9%
0.2 145
 
3.9%
0.22 144
 
3.8%
0.23 140
 
3.7%
0.27 134
 
3.6%
0.3 129
 
3.4%
0.32 125
 
3.3%
Other values (100) 2265
60.4%
ValueCountFrequency (%)
0.08 2
 
0.1%
0.085 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 5
 
0.1%
0.105 4
 
0.1%
0.11 8
 
0.2%
0.115 2
 
0.1%
0.12 27
0.7%
0.125 3
 
0.1%
0.13 31
0.8%
ValueCountFrequency (%)
0.68 12
0.3%
0.675 3
 
0.1%
0.67 15
0.4%
0.665 2
 
0.1%
0.66 14
0.4%
0.655 4
 
0.1%
0.65 10
0.3%
0.645 6
 
0.2%
0.64 16
0.4%
0.635 4
 
0.1%

citric acid
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30078196
Minimum0
Maximum0.56
Zeros61
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2023-05-10T21:25:49.151319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07
Q10.25
median0.3
Q30.37
95-th percentile0.49
Maximum0.56
Range0.56
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.11101604
Coefficient of variation (CV)0.36909143
Kurtosis0.5285375
Mean0.30078196
Median Absolute Deviation (MAD)0.06
Skewness-0.46913948
Sum1127.03
Variance0.012324562
MonotonicityNot monotonic
2023-05-10T21:25:49.258219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 221
 
5.9%
0.32 205
 
5.5%
0.28 201
 
5.4%
0.34 170
 
4.5%
0.26 168
 
4.5%
0.49 161
 
4.3%
0.29 160
 
4.3%
0.31 155
 
4.1%
0.27 153
 
4.1%
0.33 150
 
4.0%
Other values (47) 2003
53.5%
ValueCountFrequency (%)
0 61
1.6%
0.01 21
 
0.6%
0.02 26
0.7%
0.03 15
 
0.4%
0.04 20
 
0.5%
0.05 10
 
0.3%
0.06 18
 
0.5%
0.07 19
 
0.5%
0.08 23
 
0.6%
0.09 22
 
0.6%
ValueCountFrequency (%)
0.56 13
 
0.3%
0.55 4
 
0.1%
0.54 5
 
0.1%
0.53 8
 
0.2%
0.52 20
 
0.5%
0.51 12
 
0.3%
0.5 26
 
0.7%
0.49 161
4.3%
0.48 27
 
0.7%
0.47 30
 
0.8%

residual sugar
Real number (ℝ)

Distinct235
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6396851
Minimum0.6
Maximum16.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2023-05-10T21:25:49.375536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.1
Q11.6
median2.7
Q37
95-th percentile12.9
Maximum16.1
Range15.5
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation3.8770351
Coefficient of variation (CV)0.83562462
Kurtosis0.25498835
Mean4.6396851
Median Absolute Deviation (MAD)1.5
Skewness1.1355419
Sum17384.9
Variance15.031401
MonotonicityNot monotonic
2023-05-10T21:25:49.487168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6 165
 
4.4%
1.4 157
 
4.2%
1.2 148
 
3.9%
1.8 128
 
3.4%
1.5 128
 
3.4%
2 126
 
3.4%
1.3 122
 
3.3%
1.1 112
 
3.0%
1.7 110
 
2.9%
1.9 94
 
2.5%
Other values (225) 2457
65.6%
ValueCountFrequency (%)
0.6 1
 
< 0.1%
0.7 7
 
0.2%
0.8 22
 
0.6%
0.9 33
 
0.9%
0.95 2
 
0.1%
1 68
1.8%
1.05 1
 
< 0.1%
1.1 112
3.0%
1.15 2
 
0.1%
1.2 148
3.9%
ValueCountFrequency (%)
16.1 1
 
< 0.1%
16.05 4
0.1%
16 6
0.2%
15.8 4
0.1%
15.75 1
 
< 0.1%
15.7 5
0.1%
15.6 3
 
0.1%
15.55 4
0.1%
15.5 3
 
0.1%
15.4 9
0.2%

chlorides
Real number (ℝ)

Distinct82
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.047073125
Minimum0.012
Maximum0.093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2023-05-10T21:25:49.608592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.027
Q10.036
median0.044
Q30.054
95-th percentile0.081
Maximum0.093
Range0.081
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.016221817
Coefficient of variation (CV)0.34460888
Kurtosis0.25141142
Mean0.047073125
Median Absolute Deviation (MAD)0.009
Skewness0.87950089
Sum176.383
Variance0.00026314735
MonotonicityNot monotonic
2023-05-10T21:25:49.723170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.036 145
 
3.9%
0.042 132
 
3.5%
0.04 132
 
3.5%
0.034 124
 
3.3%
0.044 122
 
3.3%
0.038 118
 
3.1%
0.046 117
 
3.1%
0.037 114
 
3.0%
0.047 112
 
3.0%
0.05 109
 
2.9%
Other values (72) 2522
67.3%
ValueCountFrequency (%)
0.012 1
 
< 0.1%
0.013 1
 
< 0.1%
0.014 4
 
0.1%
0.015 3
 
0.1%
0.016 5
 
0.1%
0.017 5
 
0.1%
0.018 8
0.2%
0.019 7
0.2%
0.02 13
0.3%
0.021 15
0.4%
ValueCountFrequency (%)
0.093 11
0.3%
0.092 13
0.3%
0.091 8
 
0.2%
0.09 12
0.3%
0.089 14
0.4%
0.088 12
0.3%
0.087 16
0.4%
0.086 20
0.5%
0.085 12
0.3%
0.084 22
0.6%

free sulfur dioxide
Real number (ℝ)

Distinct70
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.038965
Minimum2
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2023-05-10T21:25:49.847706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q118
median28
Q339
95-th percentile54
Maximum61
Range59
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.948621
Coefficient of variation (CV)0.48034153
Kurtosis-0.69667067
Mean29.038965
Median Absolute Deviation (MAD)10
Skewness0.23844439
Sum108809
Variance194.56402
MonotonicityNot monotonic
2023-05-10T21:25:49.955832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 124
 
3.3%
26 116
 
3.1%
24 108
 
2.9%
31 106
 
2.8%
34 100
 
2.7%
23 98
 
2.6%
17 98
 
2.6%
25 98
 
2.6%
15 94
 
2.5%
28 94
 
2.5%
Other values (60) 2711
72.4%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 24
 
0.6%
4 27
0.7%
5 42
1.1%
6 63
1.7%
7 42
1.1%
8 53
1.4%
9 49
1.3%
10 61
1.6%
11 59
1.6%
ValueCountFrequency (%)
61 25
0.7%
60.5 2
 
0.1%
60 16
0.4%
59.5 2
 
0.1%
59 20
0.5%
58 18
0.5%
57 30
0.8%
56 20
0.5%
55 29
0.8%
54 38
1.0%

total sulfur dioxide
Real number (ℝ)

Distinct207
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.087
Minimum6
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2023-05-10T21:25:50.074169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile27
Q188
median117
Q3148
95-th percentile188
Maximum206
Range200
Interquartile range (IQR)60

Descriptive statistics

Standard deviation45.938595
Coefficient of variation (CV)0.39916406
Kurtosis-0.40008621
Mean115.087
Median Absolute Deviation (MAD)30
Skewness-0.30423365
Sum431231
Variance2110.3545
MonotonicityNot monotonic
2023-05-10T21:25:50.190560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 48
 
1.3%
113 46
 
1.2%
128 44
 
1.2%
117 43
 
1.1%
122 42
 
1.1%
114 42
 
1.1%
98 41
 
1.1%
118 40
 
1.1%
101 40
 
1.1%
124 39
 
1.0%
Other values (197) 3322
88.7%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 2
 
0.1%
8 4
 
0.1%
9 7
0.2%
10 10
0.3%
11 14
0.4%
12 12
0.3%
13 8
0.2%
14 8
0.2%
15 8
0.2%
ValueCountFrequency (%)
206 9
0.2%
205 6
 
0.2%
204 6
 
0.2%
203 5
 
0.1%
202 8
0.2%
201 13
0.3%
200 9
0.2%
199 6
 
0.2%
198 11
0.3%
197 17
0.5%

density
Real number (ℝ)

Distinct850
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99368923
Minimum0.98711
Maximum1.0006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2023-05-10T21:25:50.312347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.98962
Q10.99164
median0.9936
Q30.99566
95-th percentile0.998
Maximum1.0006
Range0.01349
Interquartile range (IQR)0.00402

Descriptive statistics

Standard deviation0.002601699
Coefficient of variation (CV)0.002618222
Kurtosis-0.81129744
Mean0.99368923
Median Absolute Deviation (MAD)0.002
Skewness0.10126486
Sum3723.3536
Variance6.7688378 × 10-6
MonotonicityNot monotonic
2023-05-10T21:25:50.433828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.992 54
 
1.4%
0.9928 49
 
1.3%
0.9944 41
 
1.1%
0.993 40
 
1.1%
0.9938 40
 
1.1%
0.9932 40
 
1.1%
0.9934 38
 
1.0%
0.9954 34
 
0.9%
0.9914 33
 
0.9%
0.994 33
 
0.9%
Other values (840) 3345
89.3%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98713 1
< 0.1%
0.98722 1
< 0.1%
0.98742 2
0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 1
< 0.1%
0.98802 1
< 0.1%
0.98815 1
< 0.1%
ValueCountFrequency (%)
1.0006 1
 
< 0.1%
1.0004 3
0.1%
1.0003 1
 
< 0.1%
1.0002 2
 
0.1%
1.0001 1
 
< 0.1%
1 2
 
0.1%
0.9998 5
0.1%
0.99975 2
 
0.1%
0.9997 2
 
0.1%
0.9996 1
 
< 0.1%

pH
Real number (ℝ)

Distinct105
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.228316
Minimum2.74
Maximum3.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2023-05-10T21:25:50.551110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile2.98
Q13.12
median3.22
Q33.33
95-th percentile3.497
Maximum3.9
Range1.16
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.15793657
Coefficient of variation (CV)0.048922277
Kurtosis0.39896667
Mean3.228316
Median Absolute Deviation (MAD)0.1
Skewness0.33375627
Sum12096.5
Variance0.02494396
MonotonicityNot monotonic
2023-05-10T21:25:50.665818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.22 110
 
2.9%
3.14 110
 
2.9%
3.16 108
 
2.9%
3.2 105
 
2.8%
3.24 102
 
2.7%
3.18 97
 
2.6%
3.15 97
 
2.6%
3.19 95
 
2.5%
3.23 94
 
2.5%
3.36 88
 
2.3%
Other values (95) 2741
73.2%
ValueCountFrequency (%)
2.74 1
 
< 0.1%
2.79 2
 
0.1%
2.8 3
0.1%
2.82 1
 
< 0.1%
2.83 3
0.1%
2.84 1
 
< 0.1%
2.85 5
0.1%
2.86 6
0.2%
2.87 5
0.1%
2.88 6
0.2%
ValueCountFrequency (%)
3.9 2
0.1%
3.85 1
< 0.1%
3.82 1
< 0.1%
3.81 1
< 0.1%
3.8 2
0.1%
3.79 1
< 0.1%
3.78 2
0.1%
3.77 2
0.1%
3.76 1
< 0.1%
3.75 2
0.1%

sulphates
Real number (ℝ)

Distinct55
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49851348
Minimum0.22
Maximum0.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2023-05-10T21:25:50.780068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.34
Q10.42
median0.49
Q30.57
95-th percentile0.7
Maximum0.77
Range0.55
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.10849455
Coefficient of variation (CV)0.21763615
Kurtosis-0.41760884
Mean0.49851348
Median Absolute Deviation (MAD)0.08
Skewness0.34035137
Sum1867.93
Variance0.011771068
MonotonicityNot monotonic
2023-05-10T21:25:50.886636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.46 154
 
4.1%
0.54 152
 
4.1%
0.5 146
 
3.9%
0.44 139
 
3.7%
0.48 133
 
3.5%
0.38 133
 
3.5%
0.47 128
 
3.4%
0.45 127
 
3.4%
0.52 121
 
3.2%
0.42 120
 
3.2%
Other values (45) 2394
63.9%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 2
 
0.1%
0.26 3
 
0.1%
0.27 10
 
0.3%
0.28 10
 
0.3%
0.29 12
 
0.3%
0.3 19
0.5%
0.31 30
0.8%
0.32 43
1.1%
ValueCountFrequency (%)
0.77 23
0.6%
0.76 23
0.6%
0.75 22
0.6%
0.74 29
0.8%
0.73 21
0.6%
0.72 29
0.8%
0.71 23
0.6%
0.7 31
0.8%
0.69 28
0.7%
0.68 42
1.1%

alcohol
Real number (ℝ)

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.71644
Minimum8.5
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2023-05-10T21:25:50.978873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8.5
5-th percentile9
Q19.5
median10.5
Q311.5
95-th percentile13
Maximum14
Range5.5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1829415
Coefficient of variation (CV)0.11038568
Kurtosis-0.72284812
Mean10.71644
Median Absolute Deviation (MAD)1
Skewness0.37906362
Sum40154.5
Variance1.3993506
MonotonicityNot monotonic
2023-05-10T21:25:51.051611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9.5 614
16.4%
10 545
14.5%
10.5 539
14.4%
11 519
13.9%
11.5 364
9.7%
12 321
8.6%
9 318
8.5%
12.5 298
8.0%
13 146
 
3.9%
13.5 45
 
1.2%
Other values (2) 38
 
1.0%
ValueCountFrequency (%)
8.5 29
 
0.8%
9 318
8.5%
9.5 614
16.4%
10 545
14.5%
10.5 539
14.4%
11 519
13.9%
11.5 364
9.7%
12 321
8.6%
12.5 298
8.0%
13 146
 
3.9%
ValueCountFrequency (%)
14 9
 
0.2%
13.5 45
 
1.2%
13 146
 
3.9%
12.5 298
8.0%
12 321
8.6%
11.5 364
9.7%
11 519
13.9%
10.5 539
14.4%
10 545
14.5%
9.5 614
16.4%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.875367
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2023-05-10T21:25:51.129124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87968051
Coefficient of variation (CV)0.1497235
Kurtosis0.14026955
Mean5.875367
Median Absolute Deviation (MAD)1
Skewness0.16719083
Sum22015
Variance0.77383779
MonotonicityNot monotonic
2023-05-10T21:25:51.199867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 1675
44.7%
5 1114
29.7%
7 688
18.4%
4 134
 
3.6%
8 121
 
3.2%
3 10
 
0.3%
9 5
 
0.1%
ValueCountFrequency (%)
3 10
 
0.3%
4 134
 
3.6%
5 1114
29.7%
6 1675
44.7%
7 688
18.4%
8 121
 
3.2%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 121
 
3.2%
7 688
18.4%
6 1675
44.7%
5 1114
29.7%
4 134
 
3.6%
3 10
 
0.3%

quality category
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.5 KiB
average
2789 
good
809 
rejected
 
144
excellent
 
5

Length

Max length9
Median length7
Mean length6.3933814
Min length4

Characters and Unicode

Total characters23956
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaverage
2nd rowaverage
3rd rowgood
4th rowgood
5th rowaverage

Common Values

ValueCountFrequency (%)
average 2789
74.4%
good 809
 
21.6%
rejected 144
 
3.8%
excellent 5
 
0.1%

Length

2023-05-10T21:25:51.290374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T21:25:51.388539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
average 2789
74.4%
good 809
 
21.6%
rejected 144
 
3.8%
excellent 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 6025
25.2%
a 5578
23.3%
g 3598
15.0%
r 2933
12.2%
v 2789
11.6%
o 1618
 
6.8%
d 953
 
4.0%
c 149
 
0.6%
t 149
 
0.6%
j 144
 
0.6%
Other values (3) 20
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23956
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6025
25.2%
a 5578
23.3%
g 3598
15.0%
r 2933
12.2%
v 2789
11.6%
o 1618
 
6.8%
d 953
 
4.0%
c 149
 
0.6%
t 149
 
0.6%
j 144
 
0.6%
Other values (3) 20
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 23956
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6025
25.2%
a 5578
23.3%
g 3598
15.0%
r 2933
12.2%
v 2789
11.6%
o 1618
 
6.8%
d 953
 
4.0%
c 149
 
0.6%
t 149
 
0.6%
j 144
 
0.6%
Other values (3) 20
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6025
25.2%
a 5578
23.3%
g 3598
15.0%
r 2933
12.2%
v 2789
11.6%
o 1618
 
6.8%
d 953
 
4.0%
c 149
 
0.6%
t 149
 
0.6%
j 144
 
0.6%
Other values (3) 20
 
0.1%

citric acid present
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.5 KiB
Present
3686 
Not Present
 
61

Length

Max length11
Median length7
Mean length7.0651188
Min length7

Characters and Unicode

Total characters26473
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Present
2nd rowPresent
3rd rowNot Present
4th rowPresent
5th rowNot Present

Common Values

ValueCountFrequency (%)
Present 3686
98.4%
Not Present 61
 
1.6%

Length

2023-05-10T21:25:51.479135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T21:25:51.577086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
present 3747
98.4%
not 61
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e 7494
28.3%
t 3808
14.4%
P 3747
14.2%
r 3747
14.2%
s 3747
14.2%
n 3747
14.2%
N 61
 
0.2%
o 61
 
0.2%
61
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22604
85.4%
Uppercase Letter 3808
 
14.4%
Space Separator 61
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7494
33.2%
t 3808
16.8%
r 3747
16.6%
s 3747
16.6%
n 3747
16.6%
o 61
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
P 3747
98.4%
N 61
 
1.6%
Space Separator
ValueCountFrequency (%)
61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26412
99.8%
Common 61
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7494
28.4%
t 3808
14.4%
P 3747
14.2%
r 3747
14.2%
s 3747
14.2%
n 3747
14.2%
N 61
 
0.2%
o 61
 
0.2%
Common
ValueCountFrequency (%)
61
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7494
28.3%
t 3808
14.4%
P 3747
14.2%
r 3747
14.2%
s 3747
14.2%
n 3747
14.2%
N 61
 
0.2%
o 61
 
0.2%
61
 
0.2%

body type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.5 KiB
Light Body
3249 
Medium Body
489 
Full Body
 
9

Length

Max length11
Median length10
Mean length10.128102
Min length9

Characters and Unicode

Total characters37950
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLight Body
2nd rowLight Body
3rd rowLight Body
4th rowLight Body
5th rowLight Body

Common Values

ValueCountFrequency (%)
Light Body 3249
86.7%
Medium Body 489
 
13.1%
Full Body 9
 
0.2%

Length

2023-05-10T21:25:51.658423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T21:25:51.764496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
body 3747
50.0%
light 3249
43.4%
medium 489
 
6.5%
full 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 4236
11.2%
3747
9.9%
B 3747
9.9%
o 3747
9.9%
y 3747
9.9%
i 3738
9.8%
L 3249
8.6%
g 3249
8.6%
h 3249
8.6%
t 3249
8.6%
Other values (6) 1992
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26709
70.4%
Uppercase Letter 7494
 
19.7%
Space Separator 3747
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 4236
15.9%
o 3747
14.0%
y 3747
14.0%
i 3738
14.0%
g 3249
12.2%
h 3249
12.2%
t 3249
12.2%
u 498
 
1.9%
e 489
 
1.8%
m 489
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
B 3747
50.0%
L 3249
43.4%
M 489
 
6.5%
F 9
 
0.1%
Space Separator
ValueCountFrequency (%)
3747
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34203
90.1%
Common 3747
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 4236
12.4%
B 3747
11.0%
o 3747
11.0%
y 3747
11.0%
i 3738
10.9%
L 3249
9.5%
g 3249
9.5%
h 3249
9.5%
t 3249
9.5%
u 498
 
1.5%
Other values (5) 1494
 
4.4%
Common
ValueCountFrequency (%)
3747
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 4236
11.2%
3747
9.9%
B 3747
9.9%
o 3747
9.9%
y 3747
9.9%
i 3738
9.8%
L 3249
8.6%
g 3249
8.6%
h 3249
8.6%
t 3249
8.6%
Other values (6) 1992
5.2%

Interactions

2023-05-10T21:25:46.972661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:34.219515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:35.348177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:36.480577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:37.583745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:38.890946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:40.071890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:41.200124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:42.527518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:43.666036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:44.764725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:45.874390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:47.064745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:34.312386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:35.442081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:36.572597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:37.820600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:38.987826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:40.165775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:41.297218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:42.622060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:43.756498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:44.857931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:45.964902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:47.158384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:34.408652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:35.535171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:36.663770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:37.919107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:39.086636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:40.259603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:41.396078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:42.717820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:43.848958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:44.949501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:46.055741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:47.247871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:34.500072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:35.625832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:36.750294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:38.011792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:39.180938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:40.350514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:41.490237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:42.808967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:43.936248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:45.038194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:46.143698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:47.346422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:34.599929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:35.725637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:36.847590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:38.113298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:39.285170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:40.449857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:41.593886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:42.909368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:44.033581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:45.136133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:46.239635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:47.640435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:34.701622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:35.827306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:36.946774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:38.218810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:39.388726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:40.552303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:41.698746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:43.011578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:44.133314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:45.236688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:46.338856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:47.737194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:34.794445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:35.920834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:37.037783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:38.314559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:39.487019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:40.643868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:41.795806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:43.106075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:44.223528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:45.328323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:46.428887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:47.838479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:34.894787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:36.021391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:37.136204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:38.418436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:39.592757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:40.743227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:41.897987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:43.207066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:44.322054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:45.427430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:46.527767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:47.932997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:34.988426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:36.116411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:37.229729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:38.515477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:39.691603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:40.838477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:42.145803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:43.300463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:44.413233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:45.520382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:46.620096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:48.021857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:35.078231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:36.206913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:37.317331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:38.609457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:39.786180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:40.927755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:42.241171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:43.390586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:44.500176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:45.608948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:46.708248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:48.111610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:35.168054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:36.296954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:37.404725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:38.703325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:39.880821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:41.018284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:42.336012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:43.481884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:44.587557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:45.695868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:46.796018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:48.200628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:35.256642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:36.387423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:37.492428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:38.795087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:39.975071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:41.107334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:42.430180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:43.571868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:44.674506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:45.784006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T21:25:46.882853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-10T21:25:51.847568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualityquality categorycitric acid presentbody type
fixed acidity1.0000.0960.256-0.0070.242-0.163-0.1010.347-0.2840.089-0.122-0.1060.0540.0960.176
volatile acidity0.0961.000-0.3380.0440.299-0.295-0.2180.2240.1580.174-0.033-0.2050.0880.3150.125
citric acid0.256-0.3381.0000.019-0.1550.1210.173-0.073-0.222-0.0390.1140.1320.1080.6220.076
residual sugar-0.0070.0440.0191.0000.0210.3030.3670.509-0.157-0.096-0.2000.0130.0790.0610.131
chlorides0.2420.299-0.1550.0211.000-0.144-0.0890.6100.2280.309-0.491-0.3250.1670.1490.236
free sulfur dioxide-0.163-0.2950.1210.303-0.1441.0000.6490.011-0.135-0.140-0.0840.1460.1370.1240.084
total sulfur dioxide-0.101-0.2180.1730.367-0.0890.6491.0000.134-0.184-0.146-0.246-0.0500.1090.2420.139
density0.3470.224-0.0730.5090.6100.0110.1341.0000.1410.240-0.751-0.3730.1910.0540.434
pH-0.2840.158-0.222-0.1570.228-0.135-0.1840.1411.0000.284-0.0080.0450.0150.1620.142
sulphates0.0890.174-0.039-0.0960.309-0.140-0.1460.2400.2841.000-0.0800.0120.0720.0690.114
alcohol-0.122-0.0330.114-0.200-0.491-0.084-0.246-0.751-0.008-0.0801.0000.4800.2410.0270.757
quality-0.106-0.2050.1320.013-0.3250.146-0.050-0.3730.0450.0120.4801.0001.0000.0750.246
quality category0.0540.0880.1080.0790.1670.1370.1090.1910.0150.0720.2411.0001.0000.0740.220
citric acid present0.0960.3150.6220.0610.1490.1240.2420.0540.1620.0690.0270.0750.0741.0000.035
body type0.1760.1250.0760.1310.2360.0840.1390.4340.1420.1140.7570.2460.2200.0351.000

Missing values

2023-05-10T21:25:48.339256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-10T21:25:48.547419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualityquality categorycitric acid presentbody type
57.400000.660000.000001.800000.0750013.0000040.000000.997803.510000.560009.500005averageNot PresentLight Body
67.900000.600000.060001.600000.0690015.0000059.000000.996403.300000.460009.500005averagePresentLight Body
77.300000.650000.000001.200000.0650015.0000021.000000.994603.390000.4700010.000007goodNot PresentLight Body
87.800000.580000.020002.000000.073009.0000018.000000.996803.360000.570009.500007goodPresentLight Body
125.600000.615000.000001.600000.0890016.0000059.000000.994303.580000.5200010.000005averageNot PresentLight Body
168.500000.280000.560001.800000.0920035.00000103.000000.996903.300000.7500010.500007goodPresentLight Body
187.400000.590000.080004.400000.086006.0000029.000000.997403.380000.500009.000004rejectedPresentLight Body
208.900000.220000.480001.800000.0770029.0000060.000000.996803.390000.530009.500006averagePresentLight Body
217.600000.390000.310002.300000.0820023.0000071.000000.998203.520000.650009.500005averagePresentLight Body
238.500000.490000.110002.300000.084009.0000067.000000.996803.170000.530009.500005averagePresentLight Body
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualityquality categorycitric acid presentbody type
64876.800000.220000.360001.200000.0520038.00000127.000000.993303.040000.540009.000005averagePresentLight Body
64884.900000.235000.2700011.750000.0300034.00000118.000000.995403.070000.500009.500006averagePresentLight Body
64896.100000.340000.290002.200000.0360025.00000100.000000.989383.060000.4400012.000006averagePresentLight Body
64905.700000.210000.320000.900000.0380038.00000121.000000.990743.240000.4600010.500006averagePresentLight Body
64916.500000.230000.380001.300000.0320029.00000112.000000.992983.290000.540009.500005averagePresentLight Body
64926.200000.210000.290001.600000.0390024.0000092.000000.991143.270000.5000011.000006averagePresentLight Body
64936.600000.320000.360008.000000.0470057.00000168.000000.994903.150000.460009.500005averagePresentLight Body
64946.500000.240000.190001.200000.0410030.00000111.000000.992542.990000.460009.500006averagePresentLight Body
64955.500000.290000.300001.100000.0220020.00000110.000000.988693.340000.3800013.000007goodPresentMedium Body
64966.000000.210000.380000.800000.0200022.0000098.000000.989413.260000.3200012.000006averagePresentLight Body